What is MLOps? Machine Learning Operations Explained

What is MLOps? Machine Learning Operations Explained

What is MLOps? MLOps is short for Machine Learning Operations, also referred to as ModelOps. MLOps is an engineering discipline that aims to unify ML systems development (dev) and ML systems deployment (ops) in order to standardize and streamline the continuous delivery of high-performing models in production.

Until recently, all of us were learning about the software development lifecycle (SDLC) and how it goes from requirement elicitation → designing → development → testing → deployment → all the way down to maintenance.

We were (and still are) studying the waterfall model, iterative model, and agile models of software development.

Now, we are at a stage where almost every organisation is trying to incorporate AI/ML into their product. This new requirement of building ML systems adds to and reforms some principles of the SDLC, giving rise to a new engineering discipline called MLOps.

This new term is creating a buzz and has given rise to new job profiles. MLOps is short for Machine Learning Operations, also referred to as ModelOps.

In this article we’ll talk about:
  • What is MLOps?
  • What problems does MLOps solve?
  • What skills do you need for MLOps?

Keep reading as I unfold each section.

What is MLOps?

If you look MLOps up on Google trends, you'll see that it is a relatively new discipline. Again, it has come to be because more organizations are trying to integrate ML systems into their products and platforms.

Here’s how I’d define MLOps:

MLOps is an engineering discipline that aims to unify ML systems development (dev) and ML systems deployment (ops) in order to standardize and streamline the continuous delivery of high-performing models in production.

Why MLOps?

Until recently, we were dealing with manageable amounts of data and a very small number of models at a small scale.

The tables are turning now, and we are embedding decision automation in a wide range of applications. This generates a lot of technical challenges that come from building and deploying ML-based systems.

In order to understand MLOps, we must first understand the ML systems lifecycle. The lifecycle involves several different teams of a data-driven organization.

From start to bottom, the following teams chip in:

  • Business development or Product team  —  defining business objective(s) with KPIs
  • Data Engineering —  data acquisition and preparation.
  • Data Science  —  architecting ML solutions and developing models.
  • IT or DevOps  —  complete deployment setup, monitoring alongside scientists.


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